基于YOLOv5的ML-AFP的YOLOv5的多对象检测,用于拥挤的道路场景
Yiming Li1, Kaiwen Wu1, Wenshuo Kang1
1College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.
Scientific reports
|October 12, 2023
概括
这项研究引入了一种改进的YOLOv5模型,用于在道路场景中检测多个物体,提高了微小目标和隐藏物体的识别. 新模型显著提高了复杂的交通场景的准确性和回忆率.
科学领域:
- 计算机视觉和机器学习
- 自主系统的人工智能
- 图像处理和模式识别.
背景情况:
- 在道路场景中的多物体检测面临着隐蔽目标和小物体 (如行人和非机动车辆) 的挑战.
- 现有的模型难以准确识别微小的目标,影响复杂的交通环境中的整体场景理解.
- 对于诸如自动驾驶和交通监控等应用来说,强大的检测系统的需求至关重要.
研究的目的:
- 提出一个改进的YOLOv5模型,用于在具有挑战性的道路场景中增强多物体检测.
- 专门解决检测微小目标和隐藏物体的局限性.
- 为了提高对象检测模型的整体准确性,回忆和平均平均精度 (mAP).
主要方法:
- 一个改进的YOLOv5模型,包括一个多级聚合特征感知 (ML-AFP) 机制.
- 增加了微型目标检测层和双头机制,以增强微小目标检测.
- 利用变焦损失来改善非最大抑制 (NMS),以处理对象封闭.
- 在ML-AFP机制中的多尺度空间特征的自适应融合,以促进特征表示.
主要成果:
- 在基准数据集 (KITTI,BDD100K) 上观察到精度,回忆率和mAP值的显著改善.
- 改进后的模型在检测小物体,如行人和非机动车辆方面表现出了卓越的性能.
- 通过Varifocal损失和改进的NMS策略,可以有效地处理目标封闭.
结论:
- 建议改进的YOLOv5模型,配备了ML-AFP机制,有效地应对拥挤道路场景中多物体检测的挑战.
- 专用层和损失函数的集成导致在检测微小和封闭物体方面取得了实质性的收益.
- 这项研究为自动驾驶和智能交通系统提供了更强大,更准确的物体检测解决方案.
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